Sr. Data Scientist, Recommendations

Launched in 2012, Tinder® revolutionized how people meet, growing from 1 match to one billion matches in just two years. This rapid growth demonstrates its ability to fulfill a fundamental human need: real connection. Today, the app has been downloaded over 630 million times, leading to over 97 billion matches, serving approximately 50 million users per month in 190 countries and 45+ languages - a scale unmatched by any other app in the category. In 2024, Tinder won four Effie Awards for its first-ever global brand campaign, “It Starts with a Swipe”™"

We work hand-in-hand, building Tinder for our members. We succeed together when we work collaboratively across functions, teams, and time zones, and think outside the box to achieve our company vision and mission.

We take accountability and strive to make a positive impact in all aspects of our business, through ownership, innovation, and a commitment to excellence.

We cultivate a culture where it’s safe to take risks. We seek out input, share honest feedback, celebrate our wins, and learn from our mistakes in order to continue improving.

We’re problem solvers, focusing on how to best move forward when faced with obstacles. We don’t dwell on the past or on the issues at hand, but instead look at how to stay agile and overcome hurdles to achieve our goals.

We are intentional about building a workplace that reflects the rich diversity of our members. By leveraging different perspectives and other ways of thinking, we build better experiences for our members and our team.

The Data Science & Analytics team thrives on data-driven insights to make more informed decisions through our insights into our member’s behavior, preferences, and common trends. We take ownership over the integrity of our data and work to improve data literacy across Tinder.

Recommendations (Recs) is core to Tinder’s experience—covering ranking, retrieval, signals, and model evaluation—to improve match quality, conversations, retention, and revenue through principled ML and experimentation.

As a Senior Data Scientist on the Recommendations (Recs) team, you will partner closely with Product, Engineering, and Machine Learning (ML) to identify and size new opportunities, strengthen existing algorithms, and shape measurement and experimentation across a two-sided marketplace. You’ll build the tooling and dashboards needed for crisp reads and health monitoring, communicate insights and tradeoffs with executive clarity, and serve as a trusted, respected partner to the Recs pod and a mentor for the broader Data Science team.

This role will be given wide latitude but high expectations for the development of analyses, models, and plans that will be shared directly with the executive team and help shape the trajectory of our product roadmap.

This is a hybrid role and requires in-office collaboration three times per week in Los Angeles, Palo Alto or San Francisco.

Factors such as scope and responsibilities of the position, candidate's work experience, education/training, job-related skills, internal peer equity, as well as market and business considerations may influence base pay offered. This salary will be subject to a geographic adjustment (according to a specific city and state), if an authorization is granted to work outside of the location listed in this posting.

If you require reasonable accommodation to complete a job application, pre-employment testing, or a job interview or to otherwise participate in the hiring process, please speak to your Talent Acquisition Partner directly.

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